Overview

Dataset statistics

Number of variables32
Number of observations8557
Missing cells58279
Missing cells (%)21.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory256.0 B

Variable types

Numeric13
Categorical19

Alerts

foulName3 has constant value "Illegal Contact" Constant
foulNFLId3 has constant value "46190.0" Constant
playDescription has a high cardinality: 8555 distinct values High cardinality
gameClock has a high cardinality: 898 distinct values High cardinality
playId is highly correlated with quarter and 2 other fieldsHigh correlation
quarter is highly correlated with playId and 2 other fieldsHigh correlation
preSnapHomeScore is highly correlated with playId and 3 other fieldsHigh correlation
preSnapVisitorScore is highly correlated with playId and 4 other fieldsHigh correlation
penaltyYards is highly correlated with playResult and 2 other fieldsHigh correlation
prePenaltyPlayResult is highly correlated with passResult and 1 other fieldsHigh correlation
playResult is highly correlated with passResult and 3 other fieldsHigh correlation
foulNFLId1 is highly correlated with foulName2 and 1 other fieldsHigh correlation
foulNFLId2 is highly correlated with defensiveTeam and 5 other fieldsHigh correlation
pff_passCoverage is highly correlated with personnelO and 3 other fieldsHigh correlation
pff_playAction is highly correlated with offenseFormation and 3 other fieldsHigh correlation
pff_passCoverageType is highly correlated with yardlineNumber and 2 other fieldsHigh correlation
possessionTeam is highly correlated with defensiveTeam and 2 other fieldsHigh correlation
offenseFormation is highly correlated with foulName2 and 5 other fieldsHigh correlation
yardlineSide is highly correlated with possessionTeam and 1 other fieldsHigh correlation
gameId is highly correlated with foulName2High correlation
down is highly correlated with yardsToGo and 1 other fieldsHigh correlation
yardsToGo is highly correlated with downHigh correlation
defensiveTeam is highly correlated with possessionTeam and 5 other fieldsHigh correlation
yardlineNumber is highly correlated with absoluteYardlineNumber and 1 other fieldsHigh correlation
passResult is highly correlated with prePenaltyPlayResult and 2 other fieldsHigh correlation
foulName1 is highly correlated with passResult and 4 other fieldsHigh correlation
foulName2 is highly correlated with gameId and 8 other fieldsHigh correlation
absoluteYardlineNumber is highly correlated with yardlineNumber and 1 other fieldsHigh correlation
personnelO is highly correlated with possessionTeam and 5 other fieldsHigh correlation
defendersInBox is highly correlated with offenseFormation and 4 other fieldsHigh correlation
personnelD is highly correlated with defensiveTeam and 5 other fieldsHigh correlation
dropBackType is highly correlated with foulName2 and 1 other fieldsHigh correlation
yardlineSide has 125 (1.5%) missing values Missing
penaltyYards has 7801 (91.2%) missing values Missing
foulName1 has 7821 (91.4%) missing values Missing
foulNFLId1 has 7821 (91.4%) missing values Missing
foulName2 has 8527 (99.6%) missing values Missing
foulNFLId2 has 8527 (99.6%) missing values Missing
foulName3 has 8556 (> 99.9%) missing values Missing
foulNFLId3 has 8556 (> 99.9%) missing values Missing
dropBackType has 528 (6.2%) missing values Missing
playDescription is uniformly distributed Uniform
preSnapHomeScore has 1648 (19.3%) zeros Zeros
preSnapVisitorScore has 1985 (23.2%) zeros Zeros
penaltyYards has 140 (1.6%) zeros Zeros
prePenaltyPlayResult has 3034 (35.5%) zeros Zeros
playResult has 2718 (31.8%) zeros Zeros

Reproduction

Analysis started2022-11-02 14:54:09.625626
Analysis finished2022-11-02 14:54:38.098363
Duration28.47 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct122
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021097803
Minimum2021090900
Maximum2021110100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:38.164096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021090900
5-th percentile2021091204
Q12021091913
median2021100312
Q32021101710
95-th percentile2021103108
Maximum2021110100
Range19200
Interquartile range (IQR)9797

Descriptive statistics

Standard deviation4970.469529
Coefficient of variation (CV)2.459291936 × 10-6
Kurtosis-1.532353115
Mean2021097803
Median Absolute Deviation (MAD)2790
Skewness-0.236761947
Sum1.72945339 × 1013
Variance24705567.34
MonotonicityIncreasing
2022-11-02T11:54:38.295592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202109090097
 
1.1%
202109261097
 
1.1%
202109130095
 
1.1%
202109120489
 
1.0%
202110240088
 
1.0%
202110030786
 
1.0%
202110031386
 
1.0%
202110170086
 
1.0%
202110240586
 
1.0%
202109191085
 
1.0%
Other values (112)7662
89.5%
ValueCountFrequency (%)
202109090097
1.1%
202109120073
0.9%
202109120181
0.9%
202109120272
0.8%
202109120376
0.9%
202109120489
1.0%
202109120579
0.9%
202109120664
0.7%
202109120767
0.8%
202109120871
0.8%
ValueCountFrequency (%)
202111010074
0.9%
202110311282
1.0%
202110311176
0.9%
202110311065
0.8%
202110310974
0.9%
202110310875
0.9%
202110310778
0.9%
202110310684
1.0%
202110310566
0.8%
202110310455
0.6%

playId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3762
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2159.218418
Minimum54
Maximum5223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:38.431466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile242
Q11127
median2156
Q33196
95-th percentile4052.4
Maximum5223
Range5169
Interquartile range (IQR)2069

Descriptive statistics

Standard deviation1222.791529
Coefficient of variation (CV)0.5663121057
Kurtosis-1.100312587
Mean2159.218418
Median Absolute Deviation (MAD)1035
Skewness0.03489208885
Sum18476432
Variance1495219.123
MonotonicityNot monotonic
2022-11-02T11:54:38.560203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7615
 
0.2%
5513
 
0.2%
10010
 
0.1%
6210
 
0.1%
326110
 
0.1%
6209
 
0.1%
29039
 
0.1%
26418
 
0.1%
25067
 
0.1%
28817
 
0.1%
Other values (3752)8459
98.9%
ValueCountFrequency (%)
546
0.1%
5513
0.2%
566
0.1%
592
 
< 0.1%
611
 
< 0.1%
6210
0.1%
633
 
< 0.1%
641
 
< 0.1%
651
 
< 0.1%
691
 
< 0.1%
ValueCountFrequency (%)
52231
< 0.1%
51531
< 0.1%
51331
< 0.1%
51081
< 0.1%
50871
< 0.1%
50731
< 0.1%
50511
< 0.1%
50361
< 0.1%
50101
< 0.1%
49531
< 0.1%

playDescription
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8555
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
TWO-POINT CONVERSION ATTEMPT. L.Jackson pass to M.Andrews is complete. ATTEMPT SUCCEEDS.
 
2
TWO-POINT CONVERSION ATTEMPT. M.Jones pass to J.Meyers is complete. ATTEMPT SUCCEEDS.
 
2
(13:54) (Shotgun) T.Lawrence pass deep right to J.Agnew to MIA 40 for 29 yards (J.Holland) [A.Van Ginkel].
 
1
(13:59) (Shotgun) T.Lawrence pass incomplete short left to J.Hollister (A.Van Ginkel).
 
1
(14:54) T.Lawrence pass short middle to C.Manhertz to JAX 36 for 11 yards (J.Baker; E.Roberts).
 
1
Other values (8550)
8550 

Length

Max length540
Median length275
Mean length97.00724553
Min length41

Characters and Unicode

Total characters830091
Distinct characters75
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8553 ?
Unique (%)> 99.9%

Sample

1st row(13:33) (Shotgun) T.Brady pass incomplete deep right to C.Godwin.
2nd row(13:18) (Shotgun) D.Prescott pass deep left to A.Cooper pushed ob at DAL 30 for 28 yards (A.Winfield).
3rd row(12:23) (Shotgun) D.Prescott pass short middle to D.Schultz to DAL 39 for 5 yards (D.White).
4th row(9:56) D.Prescott pass incomplete deep left to C.Lamb.
5th row(9:46) (Shotgun) D.Prescott pass incomplete short left to C.Lamb [L.David].

Common Values

ValueCountFrequency (%)
TWO-POINT CONVERSION ATTEMPT. L.Jackson pass to M.Andrews is complete. ATTEMPT SUCCEEDS.2
 
< 0.1%
TWO-POINT CONVERSION ATTEMPT. M.Jones pass to J.Meyers is complete. ATTEMPT SUCCEEDS.2
 
< 0.1%
(13:54) (Shotgun) T.Lawrence pass deep right to J.Agnew to MIA 40 for 29 yards (J.Holland) [A.Van Ginkel].1
 
< 0.1%
(13:59) (Shotgun) T.Lawrence pass incomplete short left to J.Hollister (A.Van Ginkel).1
 
< 0.1%
(14:54) T.Lawrence pass short middle to C.Manhertz to JAX 36 for 11 yards (J.Baker; E.Roberts).1
 
< 0.1%
(:10) (Shotgun) T.Tagovailoa pass short right to M.Gesicki ran ob at JAX 40 for 8 yards.1
 
< 0.1%
(:26) (Shotgun) T.Tagovailoa pass short right to M.Brown to JAX 48 for 7 yards (S.Griffin).1
 
< 0.1%
(:35) (Shotgun) T.Tagovailoa pass deep right to M.Hollins pushed ob at MIA 45 for 20 yards (R.Ford).1
 
< 0.1%
(13:31) (Shotgun) T.Lawrence pass short left to M.Jones to JAX 20 for 4 yards (J.Baker).1
 
< 0.1%
(:40) (Shotgun) T.Tagovailoa pass incomplete deep middle to D.Smythe.1
 
< 0.1%
Other values (8545)8545
99.9%

Length

2022-11-02T11:54:38.869094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to10844
 
8.3%
pass7951
 
6.1%
shotgun7043
 
5.4%
yards6286
 
4.8%
for6064
 
4.6%
short5909
 
4.5%
right3246
 
2.5%
left2911
 
2.2%
at2870
 
2.2%
incomplete2778
 
2.1%
Other values (3148)74889
57.3%

Most occurring characters

ValueCountFrequency (%)
122572
 
14.8%
o51263
 
6.2%
t44513
 
5.4%
s42959
 
5.2%
e39506
 
4.8%
r37834
 
4.6%
.35948
 
4.3%
a33475
 
4.0%
n28470
 
3.4%
d23183
 
2.8%
Other values (65)370368
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter460852
55.5%
Space Separator122572
 
14.8%
Uppercase Letter99625
 
12.0%
Decimal Number49616
 
6.0%
Other Punctuation49150
 
5.9%
Close Punctuation22775
 
2.7%
Open Punctuation22775
 
2.7%
Dash Punctuation2725
 
0.3%
Math Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o51263
11.1%
t44513
 
9.7%
s42959
 
9.3%
e39506
 
8.6%
r37834
 
8.2%
a33475
 
7.3%
n28470
 
6.2%
d23183
 
5.0%
h21777
 
4.7%
l21296
 
4.6%
Other values (16)116576
25.3%
Uppercase Letter
ValueCountFrequency (%)
S10934
 
11.0%
J7230
 
7.3%
T6996
 
7.0%
A6448
 
6.5%
D6373
 
6.4%
C5983
 
6.0%
N5741
 
5.8%
M5488
 
5.5%
H5247
 
5.3%
B4691
 
4.7%
Other values (16)34494
34.6%
Decimal Number
ValueCountFrequency (%)
110259
20.7%
26648
13.4%
36454
13.0%
46439
13.0%
54696
9.5%
04564
9.2%
72682
 
5.4%
62646
 
5.3%
82614
 
5.3%
92614
 
5.3%
Other Punctuation
ValueCountFrequency (%)
.35948
73.1%
:8537
 
17.4%
,4153
 
8.4%
;474
 
1.0%
#28
 
0.1%
'10
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)21921
96.3%
]854
 
3.7%
Open Punctuation
ValueCountFrequency (%)
(21921
96.3%
[854
 
3.7%
Space Separator
ValueCountFrequency (%)
122572
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2725
100.0%
Math Symbol
ValueCountFrequency (%)
=1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin560477
67.5%
Common269614
32.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o51263
 
9.1%
t44513
 
7.9%
s42959
 
7.7%
e39506
 
7.0%
r37834
 
6.8%
a33475
 
6.0%
n28470
 
5.1%
d23183
 
4.1%
h21777
 
3.9%
l21296
 
3.8%
Other values (42)216201
38.6%
Common
ValueCountFrequency (%)
122572
45.5%
.35948
 
13.3%
)21921
 
8.1%
(21921
 
8.1%
110259
 
3.8%
:8537
 
3.2%
26648
 
2.5%
36454
 
2.4%
46439
 
2.4%
54696
 
1.7%
Other values (13)24219
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII830091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122572
 
14.8%
o51263
 
6.2%
t44513
 
5.4%
s42959
 
5.2%
e39506
 
4.8%
r37834
 
4.6%
.35948
 
4.3%
a33475
 
4.0%
n28470
 
3.4%
d23183
 
2.8%
Other values (65)370368
44.6%

quarter
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
4
2446 
2
2441 
3
1837 
1
1735 
5
 
98

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8557
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
42446
28.6%
22441
28.5%
31837
21.5%
11735
20.3%
598
 
1.1%

Length

2022-11-02T11:54:38.985648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:39.092494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
42446
28.6%
22441
28.5%
31837
21.5%
11735
20.3%
598
 
1.1%

Most occurring characters

ValueCountFrequency (%)
42446
28.6%
22441
28.5%
31837
21.5%
11735
20.3%
598
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8557
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
42446
28.6%
22441
28.5%
31837
21.5%
11735
20.3%
598
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common8557
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
42446
28.6%
22441
28.5%
31837
21.5%
11735
20.3%
598
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42446
28.6%
22441
28.5%
31837
21.5%
11735
20.3%
598
 
1.1%

down
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
1
3071 
2
2826 
3
2394 
4
 
231
0
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8557
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row1
5th row3

Common Values

ValueCountFrequency (%)
13071
35.9%
22826
33.0%
32394
28.0%
4231
 
2.7%
035
 
0.4%

Length

2022-11-02T11:54:39.193501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:39.317792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
13071
35.9%
22826
33.0%
32394
28.0%
4231
 
2.7%
035
 
0.4%

Most occurring characters

ValueCountFrequency (%)
13071
35.9%
22826
33.0%
32394
28.0%
4231
 
2.7%
035
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8557
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13071
35.9%
22826
33.0%
32394
28.0%
4231
 
2.7%
035
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common8557
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13071
35.9%
22826
33.0%
32394
28.0%
4231
 
2.7%
035
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII8557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13071
35.9%
22826
33.0%
32394
28.0%
4231
 
2.7%
035
 
0.4%

yardsToGo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.724319271
Minimum0
Maximum39
Zeros35
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:39.430802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median10
Q310
95-th percentile15
Maximum39
Range39
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.913072951
Coefficient of variation (CV)0.4485247307
Kurtosis2.786182898
Mean8.724319271
Median Absolute Deviation (MAD)1
Skewness0.6844839607
Sum74654
Variance15.31213992
MonotonicityNot monotonic
2022-11-02T11:54:39.557102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
103722
43.5%
7509
 
5.9%
6494
 
5.8%
8480
 
5.6%
5472
 
5.5%
9424
 
5.0%
4410
 
4.8%
3347
 
4.1%
2296
 
3.5%
11220
 
2.6%
Other values (22)1183
 
13.8%
ValueCountFrequency (%)
035
 
0.4%
1214
2.5%
2296
3.5%
3347
4.1%
4410
4.8%
5472
5.5%
6494
5.8%
7509
5.9%
8480
5.6%
9424
5.0%
ValueCountFrequency (%)
391
 
< 0.1%
321
 
< 0.1%
303
 
< 0.1%
291
 
< 0.1%
286
0.1%
267
0.1%
2514
0.2%
247
0.1%
238
0.1%
2212
0.1%

possessionTeam
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
KC
 
339
DET
 
328
TB
 
309
MIA
 
309
NYG
 
299
Other values (27)
6973 

Length

Max length3
Median length3
Mean length2.754937478
Min length2

Characters and Unicode

Total characters23574
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowDAL
3rd rowDAL
4th rowDAL
5th rowDAL

Common Values

ValueCountFrequency (%)
KC339
 
4.0%
DET328
 
3.8%
TB309
 
3.6%
MIA309
 
3.6%
NYG299
 
3.5%
WAS298
 
3.5%
CAR294
 
3.4%
DEN292
 
3.4%
ATL289
 
3.4%
NE286
 
3.3%
Other values (22)5514
64.4%

Length

2022-11-02T11:54:39.673486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kc339
 
4.0%
det328
 
3.8%
tb309
 
3.6%
mia309
 
3.6%
nyg299
 
3.5%
was298
 
3.5%
car294
 
3.4%
den292
 
3.4%
atl289
 
3.4%
ne286
 
3.3%
Other values (22)5514
64.4%

Most occurring characters

ValueCountFrequency (%)
A2979
12.6%
N2398
 
10.2%
I2055
 
8.7%
L1859
 
7.9%
E1665
 
7.1%
C1662
 
7.1%
T1441
 
6.1%
D1139
 
4.8%
B1060
 
4.5%
H754
 
3.2%
Other values (14)6562
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter23574
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2979
12.6%
N2398
 
10.2%
I2055
 
8.7%
L1859
 
7.9%
E1665
 
7.1%
C1662
 
7.1%
T1441
 
6.1%
D1139
 
4.8%
B1060
 
4.5%
H754
 
3.2%
Other values (14)6562
27.8%

Most occurring scripts

ValueCountFrequency (%)
Latin23574
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2979
12.6%
N2398
 
10.2%
I2055
 
8.7%
L1859
 
7.9%
E1665
 
7.1%
C1662
 
7.1%
T1441
 
6.1%
D1139
 
4.8%
B1060
 
4.5%
H754
 
3.2%
Other values (14)6562
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII23574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2979
12.6%
N2398
 
10.2%
I2055
 
8.7%
L1859
 
7.9%
E1665
 
7.1%
C1662
 
7.1%
T1441
 
6.1%
D1139
 
4.8%
B1060
 
4.5%
H754
 
3.2%
Other values (14)6562
27.8%

defensiveTeam
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
CIN
 
331
TB
 
324
LA
 
318
TEN
 
316
WAS
 
308
Other values (27)
6960 

Length

Max length3
Median length3
Mean length2.738226014
Min length2

Characters and Unicode

Total characters23431
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAL
2nd rowTB
3rd rowTB
4th rowTB
5th rowTB

Common Values

ValueCountFrequency (%)
CIN331
 
3.9%
TB324
 
3.8%
LA318
 
3.7%
TEN316
 
3.7%
WAS308
 
3.6%
SEA306
 
3.6%
MIA295
 
3.4%
NYG288
 
3.4%
NE282
 
3.3%
GB279
 
3.3%
Other values (22)5510
64.4%

Length

2022-11-02T11:54:39.767688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cin331
 
3.9%
tb324
 
3.8%
la318
 
3.7%
ten316
 
3.7%
was308
 
3.6%
sea306
 
3.6%
mia295
 
3.4%
nyg288
 
3.4%
ne282
 
3.3%
gb279
 
3.3%
Other values (22)5510
64.4%

Most occurring characters

ValueCountFrequency (%)
A2953
12.6%
N2520
 
10.8%
I2161
 
9.2%
L1828
 
7.8%
E1655
 
7.1%
C1586
 
6.8%
T1341
 
5.7%
B1110
 
4.7%
D1019
 
4.3%
S844
 
3.6%
Other values (14)6414
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter23431
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2953
12.6%
N2520
 
10.8%
I2161
 
9.2%
L1828
 
7.8%
E1655
 
7.1%
C1586
 
6.8%
T1341
 
5.7%
B1110
 
4.7%
D1019
 
4.3%
S844
 
3.6%
Other values (14)6414
27.4%

Most occurring scripts

ValueCountFrequency (%)
Latin23431
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2953
12.6%
N2520
 
10.8%
I2161
 
9.2%
L1828
 
7.8%
E1655
 
7.1%
C1586
 
6.8%
T1341
 
5.7%
B1110
 
4.7%
D1019
 
4.3%
S844
 
3.6%
Other values (14)6414
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII23431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2953
12.6%
N2520
 
10.8%
I2161
 
9.2%
L1828
 
7.8%
E1655
 
7.1%
C1586
 
6.8%
T1341
 
5.7%
B1110
 
4.7%
D1019
 
4.3%
S844
 
3.6%
Other values (14)6414
27.4%

yardlineSide
Categorical

HIGH CORRELATION
MISSING

Distinct32
Distinct (%)0.4%
Missing125
Missing (%)1.5%
Memory size67.0 KiB
NYG
 
308
MIA
 
308
KC
 
307
NYJ
 
306
WAS
 
301
Other values (27)
6902 

Length

Max length3
Median length3
Mean length2.755811195
Min length2

Characters and Unicode

Total characters23237
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowDAL
3rd rowDAL
4th rowTB
5th rowTB

Common Values

ValueCountFrequency (%)
NYG308
 
3.6%
MIA308
 
3.6%
KC307
 
3.6%
NYJ306
 
3.6%
WAS301
 
3.5%
TB295
 
3.4%
DEN285
 
3.3%
CAR282
 
3.3%
SEA275
 
3.2%
DET274
 
3.2%
Other values (22)5491
64.2%

Length

2022-11-02T11:54:39.861848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nyg308
 
3.7%
mia308
 
3.7%
kc307
 
3.6%
nyj306
 
3.6%
was301
 
3.6%
tb295
 
3.5%
den285
 
3.4%
car282
 
3.3%
sea275
 
3.3%
det274
 
3.2%
Other values (22)5491
65.1%

Most occurring characters

ValueCountFrequency (%)
A2924
12.6%
N2422
 
10.4%
I2112
 
9.1%
L1756
 
7.6%
C1620
 
7.0%
E1593
 
6.9%
T1350
 
5.8%
D1051
 
4.5%
B1018
 
4.4%
S800
 
3.4%
Other values (14)6591
28.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter23237
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2924
12.6%
N2422
 
10.4%
I2112
 
9.1%
L1756
 
7.6%
C1620
 
7.0%
E1593
 
6.9%
T1350
 
5.8%
D1051
 
4.5%
B1018
 
4.4%
S800
 
3.4%
Other values (14)6591
28.4%

Most occurring scripts

ValueCountFrequency (%)
Latin23237
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2924
12.6%
N2422
 
10.4%
I2112
 
9.1%
L1756
 
7.6%
C1620
 
7.0%
E1593
 
6.9%
T1350
 
5.8%
D1051
 
4.5%
B1018
 
4.4%
S800
 
3.4%
Other values (14)6591
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII23237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2924
12.6%
N2422
 
10.4%
I2112
 
9.1%
L1756
 
7.6%
C1620
 
7.0%
E1593
 
6.9%
T1350
 
5.8%
D1051
 
4.5%
B1018
 
4.4%
S800
 
3.4%
Other values (14)6591
28.4%

yardlineNumber
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.90650929
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:39.970279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q122
median30
Q340
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.5076751
Coefficient of variation (CV)0.4182258443
Kurtosis-0.7178470225
Mean29.90650929
Median Absolute Deviation (MAD)10
Skewness-0.3446536284
Sum255910
Variance156.4419364
MonotonicityNot monotonic
2022-11-02T11:54:40.098111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25655
 
7.7%
40270
 
3.2%
38251
 
2.9%
48241
 
2.8%
49229
 
2.7%
45228
 
2.7%
33227
 
2.7%
36225
 
2.6%
32224
 
2.6%
30220
 
2.6%
Other values (40)5787
67.6%
ValueCountFrequency (%)
150
0.6%
273
0.9%
372
0.8%
455
0.6%
588
1.0%
670
0.8%
770
0.8%
877
0.9%
998
1.1%
10108
1.3%
ValueCountFrequency (%)
50125
1.5%
49229
2.7%
48241
2.8%
47212
2.5%
46184
2.2%
45228
2.7%
44216
2.5%
43196
2.3%
42217
2.5%
41202
2.4%

gameClock
Categorical

HIGH CARDINALITY

Distinct898
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
15:00
 
172
02:00
 
96
00:24
 
28
00:19
 
26
00:37
 
25
Other values (893)
8210 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters42785
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row13:33
2nd row13:18
3rd row12:23
4th row09:56
5th row09:46

Common Values

ValueCountFrequency (%)
15:00172
 
2.0%
02:0096
 
1.1%
00:2428
 
0.3%
00:1926
 
0.3%
00:3725
 
0.3%
00:2324
 
0.3%
00:3124
 
0.3%
00:1024
 
0.3%
01:5621
 
0.2%
01:5421
 
0.2%
Other values (888)8096
94.6%

Length

2022-11-02T11:54:40.211766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15:00172
 
2.0%
02:0096
 
1.1%
00:2428
 
0.3%
00:1926
 
0.3%
00:3725
 
0.3%
00:2324
 
0.3%
00:3124
 
0.3%
00:1024
 
0.3%
01:5621
 
0.2%
01:5421
 
0.2%
Other values (888)8096
94.6%

Most occurring characters

ValueCountFrequency (%)
010063
23.5%
:8557
20.0%
16124
14.3%
23479
 
8.1%
33225
 
7.5%
43141
 
7.3%
52827
 
6.6%
71363
 
3.2%
91360
 
3.2%
81328
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34228
80.0%
Other Punctuation8557
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010063
29.4%
16124
17.9%
23479
 
10.2%
33225
 
9.4%
43141
 
9.2%
52827
 
8.3%
71363
 
4.0%
91360
 
4.0%
81328
 
3.9%
61318
 
3.9%
Other Punctuation
ValueCountFrequency (%)
:8557
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42785
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010063
23.5%
:8557
20.0%
16124
14.3%
23479
 
8.1%
33225
 
7.5%
43141
 
7.3%
52827
 
6.6%
71363
 
3.2%
91360
 
3.2%
81328
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII42785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010063
23.5%
:8557
20.0%
16124
14.3%
23479
 
8.1%
33225
 
7.5%
43141
 
7.3%
52827
 
6.6%
71363
 
3.2%
91360
 
3.2%
81328
 
3.1%

preSnapHomeScore
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.66530326
Minimum0
Maximum54
Zeros1648
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:40.313986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q317
95-th percentile30
Maximum54
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.816987241
Coefficient of variation (CV)0.8415543961
Kurtosis0.04965728877
Mean11.66530326
Median Absolute Deviation (MAD)7
Skewness0.7650662064
Sum99820
Variance96.37323849
MonotonicityNot monotonic
2022-11-02T11:54:40.434976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
01648
19.3%
71310
15.3%
3707
8.3%
10675
 
7.9%
14626
 
7.3%
17585
 
6.8%
13420
 
4.9%
24358
 
4.2%
6251
 
2.9%
21248
 
2.9%
Other values (32)1729
20.2%
ValueCountFrequency (%)
01648
19.3%
28
 
0.1%
3707
8.3%
539
 
0.5%
6251
 
2.9%
71310
15.3%
950
 
0.6%
10675
7.9%
1132
 
0.4%
1261
 
0.7%
ValueCountFrequency (%)
542
 
< 0.1%
482
 
< 0.1%
4714
 
0.2%
455
 
0.1%
438
 
0.1%
4127
0.3%
405
 
0.1%
3818
0.2%
3722
0.3%
3638
0.4%

preSnapVisitorScore
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.26446184
Minimum0
Maximum44
Zeros1985
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:40.556666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q317
95-th percentile31
Maximum44
Range44
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.02541489
Coefficient of variation (CV)0.8900038929
Kurtosis-0.07558882397
Mean11.26446184
Median Absolute Deviation (MAD)7
Skewness0.7700053555
Sum96390
Variance100.5089438
MonotonicityNot monotonic
2022-11-02T11:54:40.667501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
01985
23.2%
71170
13.7%
3680
 
7.9%
14668
 
7.8%
10535
 
6.3%
17513
 
6.0%
13315
 
3.7%
21254
 
3.0%
24249
 
2.9%
6232
 
2.7%
Other values (28)1956
22.9%
ValueCountFrequency (%)
01985
23.2%
23
 
< 0.1%
3680
 
7.9%
548
 
0.6%
6232
 
2.7%
71170
13.7%
837
 
0.4%
9117
 
1.4%
10535
 
6.3%
1131
 
0.4%
ValueCountFrequency (%)
442
 
< 0.1%
4218
 
0.2%
4158
 
0.7%
3884
1.0%
3715
 
0.2%
3541
 
0.5%
3448
 
0.6%
3327
 
0.3%
31149
1.7%
3043
 
0.5%

passResult
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
C
4620 
I
2755 
S
543 
R
 
449
IN
 
190

Length

Max length2
Median length1
Mean length1.022204043
Min length1

Characters and Unicode

Total characters8747
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowC
3rd rowC
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
C4620
54.0%
I2755
32.2%
S543
 
6.3%
R449
 
5.2%
IN190
 
2.2%

Length

2022-11-02T11:54:40.776691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:40.884389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
c4620
54.0%
i2755
32.2%
s543
 
6.3%
r449
 
5.2%
in190
 
2.2%

Most occurring characters

ValueCountFrequency (%)
C4620
52.8%
I2945
33.7%
S543
 
6.2%
R449
 
5.1%
N190
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8747
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C4620
52.8%
I2945
33.7%
S543
 
6.2%
R449
 
5.1%
N190
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin8747
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C4620
52.8%
I2945
33.7%
S543
 
6.2%
R449
 
5.1%
N190
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C4620
52.8%
I2945
33.7%
S543
 
6.2%
R449
 
5.1%
N190
 
2.2%

penaltyYards
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct60
Distinct (%)7.9%
Missing7801
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean3.534391534
Minimum-18
Maximum50
Zeros140
Zeros (%)1.6%
Negative217
Negative (%)2.5%
Memory size67.0 KiB
2022-11-02T11:54:40.993521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile-10
Q1-5
median4
Q311.25
95-th percentile22.25
Maximum50
Range68
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation11.62072286
Coefficient of variation (CV)3.287899132
Kurtosis1.261978765
Mean3.534391534
Median Absolute Deviation (MAD)9
Skewness0.818334902
Sum2672
Variance135.0411998
MonotonicityNot monotonic
2022-11-02T11:54:41.117472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10145
 
1.7%
0140
 
1.6%
5120
 
1.4%
15103
 
1.2%
-526
 
0.3%
-1520
 
0.2%
418
 
0.2%
1016
 
0.2%
813
 
0.2%
1312
 
0.1%
Other values (50)143
 
1.7%
(Missing)7801
91.2%
ValueCountFrequency (%)
-183
 
< 0.1%
-1520
 
0.2%
-142
 
< 0.1%
-131
 
< 0.1%
-123
 
< 0.1%
-112
 
< 0.1%
-10145
1.7%
-92
 
< 0.1%
-81
 
< 0.1%
-71
 
< 0.1%
ValueCountFrequency (%)
501
< 0.1%
481
< 0.1%
471
< 0.1%
461
< 0.1%
452
< 0.1%
431
< 0.1%
421
< 0.1%
412
< 0.1%
401
< 0.1%
391
< 0.1%

prePenaltyPlayResult
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct98
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.500993339
Minimum-34
Maximum91
Zeros3034
Zeros (%)35.5%
Negative569
Negative (%)6.6%
Memory size67.0 KiB
2022-11-02T11:54:41.247179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-34
5-th percentile-4
Q10
median4
Q310
95-th percentile25
Maximum91
Range125
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.19551301
Coefficient of variation (CV)1.568300793
Kurtosis7.809833747
Mean6.500993339
Median Absolute Deviation (MAD)4
Skewness2.074674566
Sum55629
Variance103.9484855
MonotonicityNot monotonic
2022-11-02T11:54:41.373657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03034
35.5%
7394
 
4.6%
6378
 
4.4%
5358
 
4.2%
8333
 
3.9%
9313
 
3.7%
4300
 
3.5%
10260
 
3.0%
11244
 
2.9%
3221
 
2.6%
Other values (88)2722
31.8%
ValueCountFrequency (%)
-341
 
< 0.1%
-241
 
< 0.1%
-181
 
< 0.1%
-172
 
< 0.1%
-163
 
< 0.1%
-154
 
< 0.1%
-148
 
0.1%
-1314
0.2%
-1219
0.2%
-1121
0.2%
ValueCountFrequency (%)
911
 
< 0.1%
841
 
< 0.1%
821
 
< 0.1%
791
 
< 0.1%
771
 
< 0.1%
761
 
< 0.1%
753
< 0.1%
731
 
< 0.1%
722
< 0.1%
711
 
< 0.1%

playResult
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct102
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.563982704
Minimum-34
Maximum91
Zeros2718
Zeros (%)31.8%
Negative753
Negative (%)8.8%
Memory size67.0 KiB
2022-11-02T11:54:41.663402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-34
5-th percentile-7
Q10
median5
Q311
95-th percentile26
Maximum91
Range125
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.61126306
Coefficient of variation (CV)1.616589125
Kurtosis6.57288486
Mean6.563982704
Median Absolute Deviation (MAD)5
Skewness1.821843643
Sum56168
Variance112.5989036
MonotonicityNot monotonic
2022-11-02T11:54:41.790183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02718
31.8%
5438
 
5.1%
7379
 
4.4%
6369
 
4.3%
8317
 
3.7%
9317
 
3.7%
4302
 
3.5%
10258
 
3.0%
11239
 
2.8%
3216
 
2.5%
Other values (92)3004
35.1%
ValueCountFrequency (%)
-341
 
< 0.1%
-271
 
< 0.1%
-252
 
< 0.1%
-241
 
< 0.1%
-202
 
< 0.1%
-191
 
< 0.1%
-185
0.1%
-171
 
< 0.1%
-163
 
< 0.1%
-1512
0.1%
ValueCountFrequency (%)
911
 
< 0.1%
841
 
< 0.1%
821
 
< 0.1%
791
 
< 0.1%
771
 
< 0.1%
761
 
< 0.1%
753
< 0.1%
731
 
< 0.1%
722
< 0.1%
711
 
< 0.1%

foulName1
Categorical

HIGH CORRELATION
MISSING

Distinct29
Distinct (%)3.9%
Missing7821
Missing (%)91.4%
Memory size67.0 KiB
Defensive Pass Interference
145 
Offensive Holding
144 
Defensive Holding
76 
Roughing the Passer
61 
Defensive Offside
48 
Other values (24)
262 

Length

Max length37
Median length29
Mean length20.31929348
Min length8

Characters and Unicode

Total characters14955
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.5%

Sample

1st rowIllegal Use of Hands
2nd rowTaunting
3rd rowDefensive Pass Interference
4th rowDefensive Holding
5th rowDefensive Pass Interference

Common Values

ValueCountFrequency (%)
Defensive Pass Interference145
 
1.7%
Offensive Holding144
 
1.7%
Defensive Holding76
 
0.9%
Roughing the Passer61
 
0.7%
Defensive Offside48
 
0.6%
Illegal Use of Hands44
 
0.5%
Unnecessary Roughness44
 
0.5%
Offensive Pass Interference37
 
0.4%
Face Mask (15 Yards)24
 
0.3%
Illegal Contact21
 
0.2%
Other values (19)92
 
1.1%
(Missing)7821
91.4%

Length

2022-11-02T11:54:41.913467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
defensive269
14.3%
holding220
11.7%
pass198
 
10.5%
interference182
 
9.6%
offensive181
 
9.6%
illegal93
 
4.9%
the69
 
3.7%
roughing61
 
3.2%
passer61
 
3.2%
offside48
 
2.5%
Other values (39)505
26.8%

Most occurring characters

ValueCountFrequency (%)
e2451
16.4%
n1479
 
9.9%
s1351
 
9.0%
1151
 
7.7%
f966
 
6.5%
i920
 
6.2%
a613
 
4.1%
l576
 
3.9%
r543
 
3.6%
g529
 
3.5%
Other values (36)4376
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11965
80.0%
Uppercase Letter1743
 
11.7%
Space Separator1151
 
7.7%
Decimal Number48
 
0.3%
Close Punctuation24
 
0.2%
Open Punctuation24
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2451
20.5%
n1479
12.4%
s1351
11.3%
f966
 
8.1%
i920
 
7.7%
a613
 
5.1%
l576
 
4.8%
r543
 
4.5%
g529
 
4.4%
o517
 
4.3%
Other values (13)2020
16.9%
Uppercase Letter
ValueCountFrequency (%)
I307
17.6%
D280
16.1%
H275
15.8%
P259
14.9%
O229
13.1%
R105
 
6.0%
U93
 
5.3%
C39
 
2.2%
F34
 
2.0%
M28
 
1.6%
Other values (8)94
 
5.4%
Decimal Number
ValueCountFrequency (%)
524
50.0%
124
50.0%
Space Separator
ValueCountFrequency (%)
1151
100.0%
Close Punctuation
ValueCountFrequency (%)
)24
100.0%
Open Punctuation
ValueCountFrequency (%)
(24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13708
91.7%
Common1247
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2451
17.9%
n1479
 
10.8%
s1351
 
9.9%
f966
 
7.0%
i920
 
6.7%
a613
 
4.5%
l576
 
4.2%
r543
 
4.0%
g529
 
3.9%
o517
 
3.8%
Other values (31)3763
27.5%
Common
ValueCountFrequency (%)
1151
92.3%
524
 
1.9%
)24
 
1.9%
124
 
1.9%
(24
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII14955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2451
16.4%
n1479
 
9.9%
s1351
 
9.0%
1151
 
7.7%
f966
 
6.5%
i920
 
6.2%
a613
 
4.1%
l576
 
3.9%
r543
 
3.6%
g529
 
3.5%
Other values (36)4376
29.3%

foulNFLId1
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct486
Distinct (%)66.0%
Missing7821
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean45713.95788
Minimum30869
Maximum53957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:42.027050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30869
5-th percentile37259.5
Q142460
median45571
Q347929
95-th percentile53476.75
Maximum53957
Range23088
Interquartile range (IQR)5469

Descriptive statistics

Standard deviation4839.306151
Coefficient of variation (CV)0.1058605812
Kurtosis-0.4819010833
Mean45713.95788
Median Absolute Deviation (MAD)2742.5
Skewness-0.01906522559
Sum33645473
Variance23418884.02
MonotonicityNot monotonic
2022-11-02T11:54:42.158119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
433136
 
0.1%
386736
 
0.1%
524596
 
0.1%
461195
 
0.1%
467755
 
0.1%
448395
 
0.1%
461324
 
< 0.1%
461524
 
< 0.1%
433564
 
< 0.1%
461874
 
< 0.1%
Other values (476)687
 
8.0%
(Missing)7821
91.4%
ValueCountFrequency (%)
308691
 
< 0.1%
330842
< 0.1%
331071
 
< 0.1%
344571
 
< 0.1%
345402
< 0.1%
354433
< 0.1%
354542
< 0.1%
354621
 
< 0.1%
354702
< 0.1%
354721
 
< 0.1%
ValueCountFrequency (%)
539571
< 0.1%
539532
< 0.1%
536791
< 0.1%
536301
< 0.1%
536292
< 0.1%
536192
< 0.1%
536111
< 0.1%
536012
< 0.1%
535952
< 0.1%
535871
< 0.1%

foulName2
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)50.0%
Missing8527
Missing (%)99.6%
Memory size67.0 KiB
Defensive Pass Interference
Offensive Holding
Defensive Holding
Unnecessary Roughness
Defensive Offside
Other values (10)
12 

Length

Max length27
Median length23
Mean length19.43333333
Min length8

Characters and Unicode

Total characters583
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st rowUnnecessary Roughness
2nd rowFace Mask (15 Yards)
3rd rowTripping
4th rowDefensive Offside
5th rowRoughing the Passer

Common Values

ValueCountFrequency (%)
Defensive Pass Interference5
 
0.1%
Offensive Holding5
 
0.1%
Defensive Holding4
 
< 0.1%
Unnecessary Roughness2
 
< 0.1%
Defensive Offside2
 
< 0.1%
Roughing the Passer2
 
< 0.1%
Unsportsmanlike Conduct2
 
< 0.1%
Face Mask (15 Yards)1
 
< 0.1%
Tripping1
 
< 0.1%
Illegal Use of Hands1
 
< 0.1%
Other values (5)5
 
0.1%
(Missing)8527
99.6%

Length

2022-11-02T11:54:42.280642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
defensive11
15.9%
holding9
13.0%
interference6
 
8.7%
offensive6
 
8.7%
pass6
 
8.7%
the2
 
2.9%
illegal2
 
2.9%
conduct2
 
2.9%
unsportsmanlike2
 
2.9%
passer2
 
2.9%
Other values (17)21
30.4%

Most occurring characters

ValueCountFrequency (%)
e88
15.1%
n63
 
10.8%
s52
 
8.9%
i41
 
7.0%
39
 
6.7%
f35
 
6.0%
a23
 
3.9%
o22
 
3.8%
r21
 
3.6%
g20
 
3.4%
Other values (29)179
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter475
81.5%
Uppercase Letter65
 
11.1%
Space Separator39
 
6.7%
Decimal Number2
 
0.3%
Open Punctuation1
 
0.2%
Close Punctuation1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e88
18.5%
n63
13.3%
s52
10.9%
i41
8.6%
f35
 
7.4%
a23
 
4.8%
o22
 
4.6%
r21
 
4.4%
g20
 
4.2%
l19
 
4.0%
Other values (11)91
19.2%
Uppercase Letter
ValueCountFrequency (%)
D12
18.5%
H10
15.4%
I9
13.8%
O8
12.3%
P8
12.3%
U5
7.7%
R4
 
6.2%
C3
 
4.6%
T2
 
3.1%
F1
 
1.5%
Other values (3)3
 
4.6%
Decimal Number
ValueCountFrequency (%)
11
50.0%
51
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin540
92.6%
Common43
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e88
16.3%
n63
11.7%
s52
 
9.6%
i41
 
7.6%
f35
 
6.5%
a23
 
4.3%
o22
 
4.1%
r21
 
3.9%
g20
 
3.7%
l19
 
3.5%
Other values (24)156
28.9%
Common
ValueCountFrequency (%)
39
90.7%
(1
 
2.3%
11
 
2.3%
51
 
2.3%
)1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e88
15.1%
n63
 
10.8%
s52
 
8.9%
i41
 
7.0%
39
 
6.7%
f35
 
6.0%
a23
 
3.9%
o22
 
3.8%
r21
 
3.6%
g20
 
3.4%
Other values (29)179
30.7%

foulNFLId2
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct30
Distinct (%)100.0%
Missing8527
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean47194.86667
Minimum40107
Maximum53679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:42.385753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum40107
5-th percentile43403.95
Q144968.5
median46167
Q348082.5
95-th percentile53452.3
Maximum53679
Range13572
Interquartile range (IQR)3114

Descriptive statistics

Standard deviation3523.955494
Coefficient of variation (CV)0.07466819473
Kurtosis-0.1879646186
Mean47194.86667
Median Absolute Deviation (MAD)1745
Skewness0.6016879337
Sum1415846
Variance12418262.33
MonotonicityNot monotonic
2022-11-02T11:54:42.498619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
450091
 
< 0.1%
437221
 
< 0.1%
433271
 
< 0.1%
481181
 
< 0.1%
462691
 
< 0.1%
401071
 
< 0.1%
461191
 
< 0.1%
526241
 
< 0.1%
460761
 
< 0.1%
534491
 
< 0.1%
Other values (20)20
 
0.2%
(Missing)8527
99.6%
ValueCountFrequency (%)
401071
< 0.1%
433271
< 0.1%
434981
< 0.1%
436941
< 0.1%
437221
< 0.1%
448151
< 0.1%
448751
< 0.1%
449551
< 0.1%
450091
< 0.1%
450691
< 0.1%
ValueCountFrequency (%)
536791
< 0.1%
534551
< 0.1%
534491
< 0.1%
534461
< 0.1%
526241
< 0.1%
525871
< 0.1%
485561
< 0.1%
481181
< 0.1%
479761
< 0.1%
479131
< 0.1%

foulName3
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing8556
Missing (%)> 99.9%
Memory size67.0 KiB
Illegal Contact

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters15
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowIllegal Contact

Common Values

ValueCountFrequency (%)
Illegal Contact1
 
< 0.1%
(Missing)8556
> 99.9%

Length

2022-11-02T11:54:42.606382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:42.695961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
illegal1
50.0%
contact1
50.0%

Most occurring characters

ValueCountFrequency (%)
l3
20.0%
a2
13.3%
t2
13.3%
I1
 
6.7%
e1
 
6.7%
g1
 
6.7%
1
 
6.7%
C1
 
6.7%
o1
 
6.7%
n1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12
80.0%
Uppercase Letter2
 
13.3%
Space Separator1
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l3
25.0%
a2
16.7%
t2
16.7%
e1
 
8.3%
g1
 
8.3%
o1
 
8.3%
n1
 
8.3%
c1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
I1
50.0%
C1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14
93.3%
Common1
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l3
21.4%
a2
14.3%
t2
14.3%
I1
 
7.1%
e1
 
7.1%
g1
 
7.1%
C1
 
7.1%
o1
 
7.1%
n1
 
7.1%
c1
 
7.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l3
20.0%
a2
13.3%
t2
13.3%
I1
 
6.7%
e1
 
6.7%
g1
 
6.7%
1
 
6.7%
C1
 
6.7%
o1
 
6.7%
n1
 
6.7%

foulNFLId3
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing8556
Missing (%)> 99.9%
Memory size67.0 KiB
46190.0

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row46190.0

Common Values

ValueCountFrequency (%)
46190.01
 
< 0.1%
(Missing)8556
> 99.9%

Length

2022-11-02T11:54:42.768977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:42.854738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
46190.01
100.0%

Most occurring characters

ValueCountFrequency (%)
02
28.6%
41
14.3%
61
14.3%
11
14.3%
91
14.3%
.1
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6
85.7%
Other Punctuation1
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
33.3%
41
16.7%
61
16.7%
11
16.7%
91
16.7%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02
28.6%
41
14.3%
61
14.3%
11
14.3%
91
14.3%
.1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02
28.6%
41
14.3%
61
14.3%
11
14.3%
91
14.3%
.1
14.3%

absoluteYardlineNumber
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)1.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean59.24719495
Minimum11
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:42.944069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21
Q140
median59
Q379
95-th percentile98
Maximum109
Range98
Interquartile range (IQR)39

Descriptive statistics

Standard deviation23.65844575
Coefficient of variation (CV)0.3993175672
Kurtosis-0.9178249189
Mean59.24719495
Median Absolute Deviation (MAD)19
Skewness0.04969880969
Sum506919
Variance559.7220553
MonotonicityNot monotonic
2022-11-02T11:54:43.066714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35344
 
4.0%
85311
 
3.6%
50146
 
1.7%
54130
 
1.5%
72126
 
1.5%
42126
 
1.5%
62125
 
1.5%
60125
 
1.5%
48125
 
1.5%
43124
 
1.4%
Other values (89)6874
80.3%
ValueCountFrequency (%)
1121
 
0.2%
1241
0.5%
1325
 
0.3%
1422
 
0.3%
1552
0.6%
1630
0.4%
1739
0.5%
1834
0.4%
1953
0.6%
2068
0.8%
ValueCountFrequency (%)
10929
0.3%
10832
0.4%
10747
0.5%
10633
0.4%
10536
0.4%
10440
0.5%
10331
0.4%
10243
0.5%
10145
0.5%
10040
0.5%

offenseFormation
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing7
Missing (%)0.1%
Memory size67.0 KiB
SHOTGUN
5481 
EMPTY
1396 
SINGLEBACK
1189 
I_FORM
 
298
PISTOL
 
154
Other values (2)
 
32

Length

Max length10
Median length7
Mean length7.030760234
Min length5

Characters and Unicode

Total characters60113
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHOTGUN
2nd rowEMPTY
3rd rowSHOTGUN
4th rowSINGLEBACK
5th rowSHOTGUN

Common Values

ValueCountFrequency (%)
SHOTGUN5481
64.1%
EMPTY1396
 
16.3%
SINGLEBACK1189
 
13.9%
I_FORM298
 
3.5%
PISTOL154
 
1.8%
JUMBO30
 
0.4%
WILDCAT2
 
< 0.1%
(Missing)7
 
0.1%

Length

2022-11-02T11:54:43.177304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:43.288475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
shotgun5481
64.1%
empty1396
 
16.3%
singleback1189
 
13.9%
i_form298
 
3.5%
pistol154
 
1.8%
jumbo30
 
0.4%
wildcat2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T7033
11.7%
S6824
11.4%
G6670
11.1%
N6670
11.1%
O5963
9.9%
U5511
9.2%
H5481
9.1%
E2585
 
4.3%
M1724
 
2.9%
I1643
 
2.7%
Other values (13)10009
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter59815
99.5%
Connector Punctuation298
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T7033
11.8%
S6824
11.4%
G6670
11.2%
N6670
11.2%
O5963
10.0%
U5511
9.2%
H5481
9.2%
E2585
 
4.3%
M1724
 
2.9%
I1643
 
2.7%
Other values (12)9711
16.2%
Connector Punctuation
ValueCountFrequency (%)
_298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin59815
99.5%
Common298
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
T7033
11.8%
S6824
11.4%
G6670
11.2%
N6670
11.2%
O5963
10.0%
U5511
9.2%
H5481
9.2%
E2585
 
4.3%
M1724
 
2.9%
I1643
 
2.7%
Other values (12)9711
16.2%
Common
ValueCountFrequency (%)
_298
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII60113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T7033
11.7%
S6824
11.4%
G6670
11.1%
N6670
11.1%
O5963
9.9%
U5511
9.2%
H5481
9.1%
E2585
 
4.3%
M1724
 
2.9%
I1643
 
2.7%
Other values (13)10009
16.7%

personnelO
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Memory size67.0 KiB
1 RB, 1 TE, 3 WR
5750 
1 RB, 2 TE, 2 WR
1501 
2 RB, 1 TE, 2 WR
 
437
1 RB, 3 TE, 1 WR
 
227
1 RB, 0 TE, 4 WR
 
184
Other values (25)
 
457

Length

Max length28
Median length16
Mean length16.09151473
Min length16

Characters and Unicode

Total characters137679
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row1 RB, 1 TE, 3 WR
2nd row1 RB, 2 TE, 2 WR
3rd row0 RB, 2 TE, 3 WR
4th row1 RB, 2 TE, 2 WR
5th row1 RB, 1 TE, 3 WR

Common Values

ValueCountFrequency (%)
1 RB, 1 TE, 3 WR5750
67.2%
1 RB, 2 TE, 2 WR1501
 
17.5%
2 RB, 1 TE, 2 WR437
 
5.1%
1 RB, 3 TE, 1 WR227
 
2.7%
1 RB, 0 TE, 4 WR184
 
2.2%
2 RB, 2 TE, 1 WR110
 
1.3%
0 RB, 1 TE, 4 WR89
 
1.0%
2 RB, 0 TE, 3 WR66
 
0.8%
6 OL, 1 RB, 1 TE, 2 WR41
 
0.5%
0 RB, 2 TE, 3 WR28
 
0.3%
Other values (20)123
 
1.4%

Length

2022-11-02T11:54:43.397965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
114477
28.1%
rb8556
16.6%
te8556
16.6%
wr8553
16.6%
36109
11.8%
24371
 
8.5%
0455
 
0.9%
4274
 
0.5%
ol96
 
0.2%
695
 
0.2%
Other values (5)53
 
0.1%

Most occurring characters

ValueCountFrequency (%)
43039
31.3%
,17243
12.5%
R17112
 
12.4%
114480
 
10.5%
B8591
 
6.2%
T8556
 
6.2%
E8556
 
6.2%
W8556
 
6.2%
36109
 
4.4%
24371
 
3.2%
Other values (8)1066
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter51598
37.5%
Space Separator43039
31.3%
Decimal Number25799
18.7%
Other Punctuation17243
 
12.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R17112
33.2%
B8591
16.6%
T8556
16.6%
E8556
16.6%
W8556
16.6%
L99
 
0.2%
O96
 
0.2%
Q32
 
0.1%
Decimal Number
ValueCountFrequency (%)
114480
56.1%
36109
23.7%
24371
 
16.9%
0455
 
1.8%
4274
 
1.1%
695
 
0.4%
514
 
0.1%
71
 
< 0.1%
Space Separator
ValueCountFrequency (%)
43039
100.0%
Other Punctuation
ValueCountFrequency (%)
,17243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common86081
62.5%
Latin51598
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
43039
50.0%
,17243
20.0%
114480
 
16.8%
36109
 
7.1%
24371
 
5.1%
0455
 
0.5%
4274
 
0.3%
695
 
0.1%
514
 
< 0.1%
71
 
< 0.1%
Latin
ValueCountFrequency (%)
R17112
33.2%
B8591
16.6%
T8556
16.6%
E8556
16.6%
W8556
16.6%
L99
 
0.2%
O96
 
0.2%
Q32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII137679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43039
31.3%
,17243
12.5%
R17112
 
12.4%
114480
 
10.5%
B8591
 
6.2%
T8556
 
6.2%
E8556
 
6.2%
W8556
 
6.2%
36109
 
4.4%
24371
 
3.2%
Other values (8)1066
 
0.8%

defendersInBox
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.1%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.022339181
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.0 KiB
2022-11-02T11:54:43.490086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.005467171
Coefficient of variation (CV)0.1669562508
Kurtosis1.348878601
Mean6.022339181
Median Absolute Deviation (MAD)1
Skewness0.09826135707
Sum51491
Variance1.010964232
MonotonicityNot monotonic
2022-11-02T11:54:43.575150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
64003
46.8%
71831
21.4%
51644
19.2%
4486
 
5.7%
8475
 
5.6%
350
 
0.6%
932
 
0.4%
1113
 
0.2%
1010
 
0.1%
25
 
0.1%
(Missing)7
 
0.1%
ValueCountFrequency (%)
11
 
< 0.1%
25
 
0.1%
350
 
0.6%
4486
 
5.7%
51644
19.2%
64003
46.8%
71831
21.4%
8475
 
5.6%
932
 
0.4%
1010
 
0.1%
ValueCountFrequency (%)
1113
 
0.2%
1010
 
0.1%
932
 
0.4%
8475
 
5.6%
71831
21.4%
64003
46.8%
51644
19.2%
4486
 
5.7%
350
 
0.6%
25
 
0.1%

personnelD
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size67.0 KiB
4 DL, 2 LB, 5 DB
2420 
2 DL, 4 LB, 5 DB
1541 
3 DL, 3 LB, 5 DB
1147 
2 DL, 3 LB, 6 DB
799 
4 DL, 3 LB, 4 DB
682 
Other values (24)
1967 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters136896
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row4 DL, 2 LB, 5 DB
2nd row4 DL, 4 LB, 3 DB
3rd row3 DL, 3 LB, 5 DB
4th row4 DL, 3 LB, 4 DB
5th row3 DL, 4 LB, 4 DB

Common Values

ValueCountFrequency (%)
4 DL, 2 LB, 5 DB2420
28.3%
2 DL, 4 LB, 5 DB1541
18.0%
3 DL, 3 LB, 5 DB1147
13.4%
2 DL, 3 LB, 6 DB799
 
9.3%
4 DL, 3 LB, 4 DB682
 
8.0%
3 DL, 4 LB, 4 DB556
 
6.5%
4 DL, 1 LB, 6 DB459
 
5.4%
3 DL, 2 LB, 6 DB388
 
4.5%
1 DL, 4 LB, 6 DB209
 
2.4%
1 DL, 5 LB, 5 DB126
 
1.5%
Other values (19)229
 
2.7%

Length

2022-11-02T11:54:43.670835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dl8556
16.7%
lb8556
16.7%
db8556
16.7%
47246
14.1%
55522
10.8%
25301
10.3%
34797
9.3%
61871
 
3.6%
1863
 
1.7%
756
 
0.1%
Other values (2)12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
42780
31.2%
D17112
 
12.5%
L17112
 
12.5%
,17112
 
12.5%
B17112
 
12.5%
47246
 
5.3%
55522
 
4.0%
25301
 
3.9%
34797
 
3.5%
61871
 
1.4%
Other values (4)931
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter51336
37.5%
Space Separator42780
31.2%
Decimal Number25668
18.8%
Other Punctuation17112
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
47246
28.2%
55522
21.5%
25301
20.7%
34797
18.7%
61871
 
7.3%
1863
 
3.4%
756
 
0.2%
06
 
< 0.1%
86
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
D17112
33.3%
L17112
33.3%
B17112
33.3%
Space Separator
ValueCountFrequency (%)
42780
100.0%
Other Punctuation
ValueCountFrequency (%)
,17112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common85560
62.5%
Latin51336
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
42780
50.0%
,17112
 
20.0%
47246
 
8.5%
55522
 
6.5%
25301
 
6.2%
34797
 
5.6%
61871
 
2.2%
1863
 
1.0%
756
 
0.1%
06
 
< 0.1%
Latin
ValueCountFrequency (%)
D17112
33.3%
L17112
33.3%
B17112
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII136896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42780
31.2%
D17112
 
12.5%
L17112
 
12.5%
,17112
 
12.5%
B17112
 
12.5%
47246
 
5.3%
55522
 
4.0%
25301
 
3.9%
34797
 
3.5%
61871
 
1.4%
Other values (4)931
 
0.7%

dropBackType
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.1%
Missing528
Missing (%)6.2%
Memory size67.0 KiB
TRADITIONAL
6542 
SCRAMBLE
899 
DESIGNED_ROLLOUT_RIGHT
 
285
DESIGNED_ROLLOUT_LEFT
 
149
SCRAMBLE_ROLLOUT_RIGHT
 
125
Other values (3)
 
29

Length

Max length22
Median length11
Mean length11.44015444
Min length7

Characters and Unicode

Total characters91853
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTRADITIONAL
2nd rowTRADITIONAL
3rd rowTRADITIONAL
4th rowTRADITIONAL
5th rowTRADITIONAL

Common Values

ValueCountFrequency (%)
TRADITIONAL6542
76.5%
SCRAMBLE899
 
10.5%
DESIGNED_ROLLOUT_RIGHT285
 
3.3%
DESIGNED_ROLLOUT_LEFT149
 
1.7%
SCRAMBLE_ROLLOUT_RIGHT125
 
1.5%
SCRAMBLE_ROLLOUT_LEFT23
 
0.3%
DESIGNED_RUN5
 
0.1%
UNKNOWN1
 
< 0.1%
(Missing)528
 
6.2%

Length

2022-11-02T11:54:43.770224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:43.889905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
traditional6542
81.5%
scramble899
 
11.2%
designed_rollout_right285
 
3.5%
designed_rollout_left149
 
1.9%
scramble_rollout_right125
 
1.6%
scramble_rollout_left23
 
0.3%
designed_run5
 
0.1%
unknown1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T14248
15.5%
A14131
15.4%
I13933
15.2%
L8925
9.7%
R8586
9.3%
O7707
8.4%
D7420
8.1%
N6989
7.6%
E2097
 
2.3%
S1486
 
1.6%
Other values (10)6331
6.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter90684
98.7%
Connector Punctuation1169
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T14248
15.7%
A14131
15.6%
I13933
15.4%
L8925
9.8%
R8586
9.5%
O7707
8.5%
D7420
8.2%
N6989
7.7%
E2097
 
2.3%
S1486
 
1.6%
Other values (9)5162
 
5.7%
Connector Punctuation
ValueCountFrequency (%)
_1169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin90684
98.7%
Common1169
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T14248
15.7%
A14131
15.6%
I13933
15.4%
L8925
9.8%
R8586
9.5%
O7707
8.5%
D7420
8.2%
N6989
7.7%
E2097
 
2.3%
S1486
 
1.6%
Other values (9)5162
 
5.7%
Common
ValueCountFrequency (%)
_1169
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII91853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T14248
15.5%
A14131
15.4%
I13933
15.2%
L8925
9.7%
R8586
9.3%
O7707
8.4%
D7420
8.1%
N6989
7.6%
E2097
 
2.3%
S1486
 
1.6%
Other values (10)6331
6.9%

pff_playAction
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
0
6519 
1
2038 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8557
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
06519
76.2%
12038
 
23.8%

Length

2022-11-02T11:54:44.001377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:44.099382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
06519
76.2%
12038
 
23.8%

Most occurring characters

ValueCountFrequency (%)
06519
76.2%
12038
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8557
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06519
76.2%
12038
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common8557
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06519
76.2%
12038
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII8557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06519
76.2%
12038
 
23.8%

pff_passCoverage
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
Cover-3
2665 
Cover-1
2011 
Cover-2
1085 
Quarters
1033 
Cover-6
805 
Other values (7)
958 

Length

Max length13
Median length7
Mean length7.129367769
Min length5

Characters and Unicode

Total characters61006
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCover-1
2nd rowCover-3
3rd rowCover-3
4th rowCover-3
5th rowCover-3

Common Values

ValueCountFrequency (%)
Cover-32665
31.1%
Cover-12011
23.5%
Cover-21085
12.7%
Quarters1033
 
12.1%
Cover-6805
 
9.4%
Red Zone376
 
4.4%
Cover-0270
 
3.2%
2-Man200
 
2.3%
Bracket47
 
0.5%
Prevent32
 
0.4%
Other values (2)33
 
0.4%

Length

2022-11-02T11:54:44.191252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cover-32665
29.7%
cover-12011
22.4%
cover-21085
12.1%
quarters1033
 
11.5%
cover-6805
 
9.0%
red376
 
4.2%
zone376
 
4.2%
cover-0270
 
3.0%
2-man200
 
2.2%
bracket47
 
0.5%
Other values (4)90
 
1.0%

Most occurring characters

ValueCountFrequency (%)
r8981
14.7%
e8773
14.4%
o7245
11.9%
-7036
11.5%
v6868
11.3%
C6836
11.2%
32665
 
4.4%
12011
 
3.3%
a1313
 
2.2%
21285
 
2.1%
Other values (20)7993
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37575
61.6%
Uppercase Letter8958
 
14.7%
Dash Punctuation7036
 
11.5%
Decimal Number7036
 
11.5%
Space Separator401
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r8981
23.9%
e8773
23.3%
o7245
19.3%
v6868
18.3%
a1313
 
3.5%
t1112
 
3.0%
s1049
 
2.8%
u1041
 
2.8%
n641
 
1.7%
d376
 
1.0%
Other values (4)176
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C6836
76.3%
Q1033
 
11.5%
Z376
 
4.2%
R376
 
4.2%
M208
 
2.3%
B47
 
0.5%
P32
 
0.4%
G25
 
0.3%
L25
 
0.3%
Decimal Number
ValueCountFrequency (%)
32665
37.9%
12011
28.6%
21285
18.3%
6805
 
11.4%
0270
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
-7036
100.0%
Space Separator
ValueCountFrequency (%)
401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin46533
76.3%
Common14473
 
23.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r8981
19.3%
e8773
18.9%
o7245
15.6%
v6868
14.8%
C6836
14.7%
a1313
 
2.8%
t1112
 
2.4%
s1049
 
2.3%
u1041
 
2.2%
Q1033
 
2.2%
Other values (13)2282
 
4.9%
Common
ValueCountFrequency (%)
-7036
48.6%
32665
 
18.4%
12011
 
13.9%
21285
 
8.9%
6805
 
5.6%
401
 
2.8%
0270
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII61006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r8981
14.7%
e8773
14.4%
o7245
11.9%
-7036
11.5%
v6868
11.3%
C6836
11.2%
32665
 
4.4%
12011
 
3.3%
a1313
 
2.2%
21285
 
2.1%
Other values (20)7993
13.1%

pff_passCoverageType
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
Zone
5588 
Man
2481 
Other
 
488

Length

Max length5
Median length4
Mean length3.76709127
Min length3

Characters and Unicode

Total characters32235
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMan
2nd rowZone
3rd rowZone
4th rowZone
5th rowZone

Common Values

ValueCountFrequency (%)
Zone5588
65.3%
Man2481
29.0%
Other488
 
5.7%

Length

2022-11-02T11:54:44.298750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:54:44.570726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
zone5588
65.3%
man2481
29.0%
other488
 
5.7%

Most occurring characters

ValueCountFrequency (%)
n8069
25.0%
e6076
18.8%
Z5588
17.3%
o5588
17.3%
M2481
 
7.7%
a2481
 
7.7%
O488
 
1.5%
t488
 
1.5%
h488
 
1.5%
r488
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23678
73.5%
Uppercase Letter8557
 
26.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n8069
34.1%
e6076
25.7%
o5588
23.6%
a2481
 
10.5%
t488
 
2.1%
h488
 
2.1%
r488
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
Z5588
65.3%
M2481
29.0%
O488
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin32235
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n8069
25.0%
e6076
18.8%
Z5588
17.3%
o5588
17.3%
M2481
 
7.7%
a2481
 
7.7%
O488
 
1.5%
t488
 
1.5%
h488
 
1.5%
r488
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII32235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n8069
25.0%
e6076
18.8%
Z5588
17.3%
o5588
17.3%
M2481
 
7.7%
a2481
 
7.7%
O488
 
1.5%
t488
 
1.5%
h488
 
1.5%
r488
 
1.5%

Interactions

2022-11-02T11:54:35.175858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:17.200119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.688733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.409776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.912736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.318671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.871317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.273240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:27.881622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.264300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.732333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.351657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.641470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.285864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:17.328468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.801329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.528337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.037966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.447648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.993729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.394043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.005858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.380784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.876080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.460901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.758348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.382386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:17.439948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.900836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.642834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.143497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.553339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.110508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.499099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.112685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.482951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.989682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.574201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.867164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.488778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:17.559227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.009652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.761173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.261301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.668711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.228430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.613848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.225008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.596259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:31.114849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.683694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.986909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.586518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:17.674829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.108079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.874394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.371454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.942626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.339977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.730573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.328393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.703038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:31.405519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.777275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.093093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.684858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:17.793882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.222146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.992882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.474572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.041138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.450160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.844101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.433379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.819245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:31.500491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.875232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.198065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.779465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:17.899882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.324127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.102773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.573586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.135614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.557319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.957445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.532497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.923059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:31.607224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.968640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.298331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.882774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.025477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.441874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.218955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.686293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.239102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.664500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:27.078343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.645066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.032419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:31.719526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.058212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.408828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.988876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.143121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.553190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.338474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.803659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.347835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.772519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:27.194713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.751121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.145399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:31.839616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.150449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.517289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:36.089624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.256500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.674325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.453092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:22.910032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.463290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.875895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:27.299495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.858355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.259116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:31.948334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.252038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.624111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:36.187362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.367053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:19.788381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.573743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.014658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.563752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:25.979569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:27.410370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:28.966852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.363372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.059693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.351764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.722800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:36.281507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.467038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.213761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.686654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.110323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.663822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.071864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:27.669631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.061636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.470462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.156550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.455518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:34.985711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:36.391363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:18.578476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:20.313160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:21.796076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:23.211035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:24.766557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:26.173815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:27.777508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:29.163474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:30.606542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:32.254071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:33.547431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:54:35.081241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T11:54:44.663695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T11:54:44.863871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T11:54:45.064218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T11:54:45.266547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T11:54:45.471921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T11:54:36.605068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T11:54:37.372422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T11:54:37.682324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T11:54:37.916948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdplayDescriptionquarterdownyardsToGopossessionTeamdefensiveTeamyardlineSideyardlineNumbergameClockpreSnapHomeScorepreSnapVisitorScorepassResultpenaltyYardsprePenaltyPlayResultplayResultfoulName1foulNFLId1foulName2foulNFLId2foulName3foulNFLId3absoluteYardlineNumberoffenseFormationpersonnelOdefendersInBoxpersonnelDdropBackTypepff_playActionpff_passCoveragepff_passCoverageType
0202109090097(13:33) (Shotgun) T.Brady pass incomplete deep right to C.Godwin.132TBDALTB3313:3300INaN00NaNNaNNaNNaNNaNNaN43.0SHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DBTRADITIONAL0Cover-1Man
12021090900137(13:18) (Shotgun) D.Prescott pass deep left to A.Cooper pushed ob at DAL 30 for 28 yards (A.Winfield).1110DALTBDAL213:1800CNaN2828NaNNaNNaNNaNNaNNaN108.0EMPTY1 RB, 2 TE, 2 WR6.04 DL, 4 LB, 3 DBTRADITIONAL0Cover-3Zone
22021090900187(12:23) (Shotgun) D.Prescott pass short middle to D.Schultz to DAL 39 for 5 yards (D.White).126DALTBDAL3412:2300CNaN55NaNNaNNaNNaNNaNNaN76.0SHOTGUN0 RB, 2 TE, 3 WR6.03 DL, 3 LB, 5 DBTRADITIONAL0Cover-3Zone
32021090900282(9:56) D.Prescott pass incomplete deep left to C.Lamb.1110DALTBTB3909:5600INaN00NaNNaNNaNNaNNaNNaN49.0SINGLEBACK1 RB, 2 TE, 2 WR6.04 DL, 3 LB, 4 DBTRADITIONAL1Cover-3Zone
42021090900349(9:46) (Shotgun) D.Prescott pass incomplete short left to C.Lamb [L.David].1315DALTBTB4409:4600INaN00NaNNaNNaNNaNNaNNaN54.0SHOTGUN1 RB, 1 TE, 3 WR7.03 DL, 4 LB, 4 DBTRADITIONAL0Cover-3Zone
52021090900410(8:53) (Shotgun) T.Brady pass short middle to M.Evans to TB 21 for 10 yards (T.Diggs).125TBDALTB1108:5300CNaN1010NaNNaNNaNNaNNaNNaN21.0EMPTY1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DBTRADITIONAL0Cover-1Man
62021090900434(8:24) (No Huddle, Shotgun) T.Brady pass incomplete short left to M.Evans.1110TBDALTB2108:2400INaN00NaNNaNNaNNaNNaNNaN31.0SHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DBTRADITIONAL0Cover-3Zone
72021090900456(8:20) (Shotgun) T.Brady pass deep middle to R.Gronkowski to TB 40 for 19 yards (D.Kazee).1210TBDALTB2108:2000CNaN1919NaNNaNNaNNaNNaNNaN31.0SHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DBTRADITIONAL0Cover-6Zone
82021090900480(7:53) (No Huddle, Shotgun) T.Brady pass deep right to A.Brown ran ob at DAL 32 for 28 yards (A.Brown).1110TBDALTB4007:5300CNaN2828NaNNaNNaNNaNNaNNaN50.0SHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DBTRADITIONAL0Cover-3Zone
92021090900509(7:30) (No Huddle, Shotgun) T.Brady pass short right to A.Brown pushed ob at DAL 16 for 16 yards (A.Brown; K.Neal).1110TBDALDAL3207:3000CNaN1616NaNNaNNaNNaNNaNNaN78.0SHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DBTRADITIONAL0QuartersZone

Last rows

gameIdplayIdplayDescriptionquarterdownyardsToGopossessionTeamdefensiveTeamyardlineSideyardlineNumbergameClockpreSnapHomeScorepreSnapVisitorScorepassResultpenaltyYardsprePenaltyPlayResultplayResultfoulName1foulNFLId1foulName2foulNFLId2foulName3foulNFLId3absoluteYardlineNumberoffenseFormationpersonnelOdefendersInBoxpersonnelDdropBackTypepff_playActionpff_passCoveragepff_passCoverageType
854720211101003955(4:54) (Shotgun) D.Jones pass incomplete short middle to D.Booker.437NYGKCNYG4204:541717INaN00NaNNaNNaNNaNNaNNaN52.0SHOTGUN1 RB, 1 TE, 3 WR5.04 DL, 1 LB, 6 DBSCRAMBLE0Cover-1Man
854820211101004016(4:41) (Shotgun) P.Mahomes pass incomplete short left to T.Kelce [O.Ximines]. PENALTY on KC-O.Brown, Offensive Holding, 10 yards, enforced at KC 29 - No Play.4110KCNYGKC2904:411717I-10.00-10Offensive Holding46152.0NaNNaNNaNNaN81.0EMPTY1 RB, 1 TE, 3 WR5.02 DL, 4 LB, 5 DBNaN0Cover-3Zone
854920211101004049(4:34) (Shotgun) P.Mahomes pass incomplete short right [O.Ximines].4120KCNYGKC1904:341717INaN00NaNNaNNaNNaNNaNNaN91.0SHOTGUN1 RB, 1 TE, 3 WR5.01 DL, 4 LB, 6 DBDESIGNED_ROLLOUT_RIGHT0Cover-2Zone
855020211101004071(4:29) (Shotgun) P.Mahomes pass short right intended for B.Pringle INTERCEPTED by D.Holmes at KC 34. D.Holmes to KC 34 for no gain (B.Pringle). PENALTY on NYG-O.Ximines, Defensive Offside, 5 yards, enforced at KC 19 - No Play.4220KCNYGKC1904:291717IN5.000Defensive Offside47878.0NaNNaNNaNNaN91.0SHOTGUN1 RB, 1 TE, 3 WR4.01 DL, 4 LB, 6 DBNaN0Cover-2Zone
855120211101004113(4:22) (Shotgun) P.Mahomes pass short right to T.Kelce to KC 38 for 14 yards (L.Ryan). PENALTY on NYG-T.Crowder, Face Mask (15 Yards), 15 yards, enforced at KC 38.4215KCNYGKC2404:221717C15.01429Face Mask (15 Yards)52663.0NaNNaNNaNNaN86.0SHOTGUN1 RB, 1 TE, 3 WR4.01 DL, 4 LB, 6 DBTRADITIONAL0Cover-2Zone
855220211101004310(1:56) (Shotgun) P.Mahomes sacked at NYG 16 for -8 yards (K.Crossen).438KCNYGNYG801:561717SNaN-8-8NaNNaNNaNNaNNaNNaN18.0SHOTGUN1 RB, 1 TE, 3 WR4.01 DL, 3 LB, 7 DBSCRAMBLE0BracketOther
855320211101004363(1:07) (Shotgun) D.Jones pass short right to E.Engram pushed ob at NYG 28 for 3 yards (L.Sneed).4110NYGKCNYG2501:072017CNaN33NaNNaNNaNNaNNaNNaN35.0SHOTGUN1 RB, 1 TE, 3 WR5.04 DL, 1 LB, 6 DBSCRAMBLE0Cover-2Zone
855420211101004392(1:01) (No Huddle, Shotgun) D.Jones sacked at NYG 20 for -8 yards (C.Jones).427NYGKCNYG2801:012017SNaN-8-8NaNNaNNaNNaNNaNNaN38.0SHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 1 LB, 6 DBTRADITIONAL0Cover-2Zone
855520211101004411(:39) (No Huddle, Shotgun) D.Jones pass incomplete short right to E.Engram.4315NYGKCNYG2000:392017INaN00NaNNaNNaNNaNNaNNaN30.0SHOTGUN1 RB, 1 TE, 3 WR5.04 DL, 1 LB, 6 DBTRADITIONAL0Cover-2Zone
855620211101004433(:35) (Shotgun) D.Jones sacked at NYG 14 for -6 yards (F.Clark). FUMBLES (F.Clark) [F.Clark], recovered by NYG-B.Price at NYG 15.4415NYGKCNYG2000:352017SNaN-5-5NaNNaNNaNNaNNaNNaN30.0SHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 1 LB, 6 DBTRADITIONAL0QuartersZone